MaskFormer [1]}.
This is a recently proposed method for supervised semantic segmentation. Unlike a fully convolution network that models the semantic segmentation as the pixel-wise classification problem, MaskFormer disentangles the semantic segmentation into two sub-tasks: predicting the segments at first and then classifying the category of each segment. We suggest that the predicted segments by MaskFormer can be used as the mask proposals, and we empirically demonstrate (see tab:proposalgen) that the MaskFormer trained on seen classes can produce high-quality mask proposals on the unseen classes. Therefore, we take this advantage of MaskFormer as our default mask proposal generator.
